PMID- 36037455
OWN - NLM
STAT- MEDLINE
DCOM- 20230406
LR  - 20230509
IS  - 1558-254X (Electronic)
IS  - 0278-0062 (Linking)
VI  - 42
IP  - 1
DP  - 2023 Jan
TI  - A Deep Learning Method for Motion Artifact Correction in Intravascular 
      Photoacoustic Image Sequence.
PG  - 66-78
LID - 10.1109/TMI.2022.3202910 [doi]
AB  - In vivo application of intravascular photoacoustic (IVPA) imaging for coronary 
      arteries is hampered by motion artifacts associated with the cardiac cycle. 
      Gating is a common strategy to mitigate motion artifacts. However, a large amount 
      of diagnostically valuable information might be lost due to one frame per cycle. 
      In this work, we present a deep learning-based method for directly correcting 
      motion artifacts in non-gated IVPA pullback sequences. The raw signal frames are 
      classified into dynamic and static frames by clustering. Then, a neural network 
      named Motion Artifact Correction (MAC)-Net is designed to correct motion in 
      dynamic frames. Given the lack of the ground truth information on the underlying 
      dynamics of coronary arteries, we trained and tested the network using a 
      computer-generated dataset. Based on the results, it has been observed that the 
      trained network can directly correct motion in successive frames while preserving 
      the original structures without discarding any frames. The improvement in the 
      visual effect of the longitudinal view has been demonstrated based on 
      quantitative evaluation of the inter-frame dissimilarity. The comparison results 
      validated the motion-suppression ability of our method comparable to gating and 
      image registration-based non-learning methods, while maintaining the integrity of 
      the pullbacks without image preprocessing. Experimental results from in vivo 
      intravascular ultrasound and optical coherence tomography pullbacks validated the 
      feasibility of our method in the in vivo intracoronary imaging scenario.
FAU - Zheng, Sun
AU  - Zheng S
FAU - Jiejie, Du
AU  - Jiejie D
FAU - Yue, Yao
AU  - Yue Y
FAU - Qi, Meng
AU  - Qi M
FAU - Huifeng, Sun
AU  - Huifeng S
LA  - eng
PT  - Journal Article
PT  - Research Support, Non-U.S. Gov't
DEP - 20221229
PL  - United States
TA  - IEEE Trans Med Imaging
JT  - IEEE transactions on medical imaging
JID - 8310780
SB  - IM
MH  - *Artifacts
MH  - *Deep Learning
MH  - Motion
MH  - Heart
MH  - Coronary Vessels/diagnostic imaging
MH  - Image Processing, Computer-Assisted/methods
EDAT- 2022/08/30 06:00
MHDA- 2023/04/06 06:42
CRDT- 2022/08/29 16:53
PHST- 2023/04/06 06:42 [medline]
PHST- 2022/08/30 06:00 [pubmed]
PHST- 2022/08/29 16:53 [entrez]
AID - 10.1109/TMI.2022.3202910 [doi]
PST - ppublish
SO  - IEEE Trans Med Imaging. 2023 Jan;42(1):66-78. doi: 10.1109/TMI.2022.3202910. Epub 
      2022 Dec 29.